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arXiv 2102.01327quant-ph
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非马尔可夫量子过程的实验表征

Experimental characterisation of a non-Markovian quantum process

K. Goswami, C. Giarmatzi, C. Monterola, S. Shrapnel, J. Romero, F. Costa

摘要:

每个量子系统都与环境耦合。这样的系统-环境相互作用导致不同时间的量子操作之间的时间相关性,从而导致非马尔可夫噪声。原则上,非马尔可夫噪声的完整表征需要多时间过程矩阵的层析成像,这在计算上和实验上都是苛刻的。在本文中,我们提出了一个更有效的解决方案。我们采用机器学习模型来估计非Markovianity的数量,如通过信息理论测量量化的,具有断层扫描不完整的测量。我们在量子光学实验上测试了我们的模型,并且我们能够以$90\%$的精度预测非马尔可夫度量。我们的实验为有效检测大规模量子计算机中出现的非马尔可夫噪声铺平了道路。

英文摘要:

Every quantum system is coupled to an environment. Such system-environment interaction leads to temporal correlation between quantum operations at different times, resulting in non-Markovian noise. In principle, a full characterisation of non-Markovian noise requires tomography of a multi-time processes matrix, which is both computationally and experimentally demanding. In this paper, we propose a more efficient solution. We employ machine learning models to estimate the amount of non-Markovianity, as quantified by an information-theoretic measure, with tomographically incomplete measurement. We test our model on a quantum optical experiment, and we are able to predict the non-Markovianity measure with $90\%$ accuracy. Our experiment paves the way for efficient detection of non-Markovian noise appearing in large scale quantum computers.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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